Loss Values Research Articles

Burned Area Detection with Sentinel-2A Data: Using Deep Learning Techniques with eXplainable Artificial Intelligence

Abstract. Annually, a considerable quantity of forest is burned on a global scale. Therefore, it is essential to obtain precise and fast information regarding the size of burned regions in order to effectively monitor the adverse consequences of wildfires. The objective of this investigation is to indicate the effectiveness and usefulness of a deep learning (DL) architecture, such as Convolutional Neural Networks (CNNs), in the mapping of areas affected by fire, employing an eXplainable artificial intelligence (XAI) algorithm known as SHapley Additive exPlanations (SHAP) with accuracy evaluation criteria. Furthermore, this paper presents the evaluation of the Çanakkale-Kizilkeçili village wildfire. The research investigated the impacts of a variety of spectral indices, including the normalized burn index (NBR), differentiated normalized burn index (dNBR), Green-Red Vegetation Index (GRVI), simple ratio vegetation index (RVI), and normalized difference vegetation index (NDVI). At the end of the training process, the model achieved a training accuracy of approximately 0.99, with model loss values converging to approximately 0.1. The findings of the burned area identification analysis indicate that by incorporating spectral indices as supplementary information, the CNN model achieved a high level of accuracy, with an overall accuracy of 98.88% and a Kappa Coefficient of 0.98. Additionally, the SHAP technique was employed to gain insights into the output of the models. The feature importances of the spectral bands were determined through the SHAP analysis of the CNN model. Hence, the significance of the auxiliary data generated by the NBR, dNBR, and NDVI indices was identified as being the highest among the original bands and auxiliary data employed in this investigation.

Integrating Deep Learning and Energy Management Standards for Enhanced Solar–Hydrogen Systems: A Study Using MobileNetV2, InceptionV3, and ISO 50001:2018

This study addresses the growing need for effective energy management solutions in university settings, with particular emphasis on solar–hydrogen systems. The study’s purpose is to explore the integration of deep learning models, specifically MobileNetV2 and InceptionV3, in enhancing fault detection capabilities in AIoT-based environments, while also customizing ISO 50001:2018 standards to align with the unique energy management needs of academic institutions. Our research employs comparative analysis of the two deep learning models in terms of their performance in detecting solar panel defects and assessing accuracy, loss values, and computational efficiency. The findings reveal that MobileNetV2 achieves 80% accuracy, making it suitable for resource-constrained environments, while InceptionV3 demonstrates superior accuracy of 90% but requires more computational resources. The study concludes that both models offer distinct advantages based on application scenarios, emphasizing the importance of balancing accuracy and efficiency when selecting appropriate models for solar–hydrogen system management. This research highlights the critical role of continuous improvement and leadership commitment in the successful implementation of energy management standards in universities.

Climate Change May Increase the Impact of Coastal Flooding on Carbon Storage in China’s Coastal Terrestrial Ecosystems

Climate warming exacerbates the deterioration of soil and degradation of vegetation caused by coastal flooding, impairing ecosystem climate-regulating functions. This will elevate the risk of carbon storage (CS) loss, further intensifying climate change. To delve deeper into this aspect, we aimed to integrate future land use/land cover changes and global mean sea-level rise to assess the impact of coastal floods on terrestrial CS under the effects of climate change. We compared the 10-year (RP10) and 100-year (RP100) return-period floods in 2020 with projected scenarios for 2050 under SSP1-26, SSP2-45, SSP3-70, and SSP5-85. The study findings indicate that CS loss caused by coastal flooding in China’s coastal zones was 198.71 Tg (RP10) and 263.46 Tg (RP100) in 2020. In 2050, under the SSP1-26, SSP2-45, and SSP3-70 scenarios, the CS loss is projected to increase sequentially, underscoring the importance of implementing globally coordinated strategies for mitigating climate change to effectively manage coastal flooding. The value of CS loss is expected to increase in 2050, with an anticipated rise of 97–525% (RP10) and 91–498% (RP100). This highlights the essential need to include coastal flood-induced CS changes in carbon emission management and coastal climate risk assessments.

Thermally Deposited Pt/InSe/Nb2O5/Ag Stacked Layers Designed as Negative Capacitance Sources and Antennas for 5 G/6 G Networks

Herein, Pt/InSe/Nb2O5/Ag (ISNO) stacked layers are designed as multichannel 5 G/6 G network antennas and negative capacitance (NC) sources. The devices are fabricated using thermal deposition technique to coat Pt/InSe thin films with transparent Nb2O5 dielectric windows. While Pt thin films induce the crystallinity of InSe, the Nb2O5 layer remains amorphous. The impedance spectroscopy studies show that the Pt/InSe/Ag (IS) and (ISNO) antenna channels exhibit resonance–antiresonance peaks at driving frequencies of ≈0.4 and ≈1.1 GHz, respectively. Both the IS and ISNO antenna channels exhibit the NC effect in a wide range of frequency domain. In addition, ISNO antennas show signal transmission above 1.70 GHz with a return loss value of 28 dB and voltage standing wave ratios of 1.20. The range of frequencies over which the antenna performs well (bandwidth) extends from 1.54 to 1.80 GHz. Practical tests of one‐port and two‐port reflection/transmission using a network analyzer operative in the frequency domain of 0.01–6.0 GHz highlight the system's differing isolation and coupling characteristics at 2.43 and 6.0 GHz, respectively. The high levels of isolation at 6.0 GHz assure the suitability of the system for applications requiring minimal reverse transmission and high signal integrity. The NC effect, wide bandwidth, and high signal isolation characteristics are preferable in electronics and 5 G/6 G networks.

Crop Leaf Type Classification and Multi-Class Leaf Disease Identification with Tangent Hunter Prey Optimization-Based LeNet

The early detection and accurate rate of classification of plant leaf disease are more important for reliable and good agriculture, which prevents unwanted financial loss and resources. In this study, Tangent Hunter Prey Optimization-based LeNet (THPO-LeNet) is utilized for crop leaf classification and multi-class plant leaf disease identification. In this case, the input image that goes through the image pre-processing step is obtained from the plant village dataset. An adaptive Wiener filter is applied at the pre-processing stage to reduce needless mistakes and improve image quality. The pre-processed image is then sent to the leaf segmentation step, where a Mask Region-based Convolution Neural Network (Mask R-CNN) performs the segmentation. Consequently, image augmentation is performed using techniques such as scaling, rotation, translation, flipping, contrast, saturation, and hue. Afterwards, first-level classification plant leaf classification is processed by LeNet, which is optimized by Tangent Hunter Prey Optimization (THPO). The THPO is the incorporation of a Tangent Search Algorithm (TSA) and Hunter–Prey Optimizer (HPO). At last, the second-level classification of plant leaf disease is conducted by THPO-LeNet. Furthermore, the efficiency of THPO-LeNet is examined based on measures, like accuracy, Negative Predictive Value (NPV), True Positive Rate (TPR), True Negative Rate (TNR), Positive Predictive Value (PPV), loss value, and False Positive Rate (FPR), and the value attained is 95.68%, 92.50%, 91.48%, 92.25%, 92.54%, 8.321%, and 7.754%, respectively.

Natural Language Processing and Machine Learning-Based Solution of Cold Start Problem Using Collaborative Filtering Approach

In today’s digital era, the abundance of online services presents users with a daunting array of choices, spanning from streaming platforms to e-commerce websites, leading to decision fatigue. Recommendation algorithms play a pivotal role in aiding users in navigating this plethora of options, among which collaborative filtering (CF) stands out as a prevalent technique. However, CF encounters several challenges, including scalability issues, privacy implications, and the well-known cold start problem. This study endeavors to mitigate the cold start problem by harnessing the capabilities of natural language processing (NLP) applied to user-generated reviews. A unique methodology is introduced, integrating both supervised and unsupervised NLP approaches facilitated by sci-kit learn, utilizing benchmark datasets across diverse domains. This study offers scientific contributions through its novel approach, ensuring rigor, precision, scalability, and real-world relevance. It tackles the cold start problem in recommendation systems by combining natural language processing (NLP) with machine learning and collaborative filtering techniques, addressing data sparsity effectively. This study emphasizes reproducibility and accuracy while proposing an advanced solution that improves personalization in recommendation models. The proposed NLP-based strategy enhances the quality of user-generated content, consequently refining the accuracy of Collaborative Filtering-Based Recommender Systems (CFBRSs). The authors conducted experiments to test the performance of the proposed approach on benchmark datasets like MovieLens, Jester, Book-Crossing, Last.fm, Amazon Product Reviews, Yelp, Netflix Prize, Goodreads, IMDb (Internet movie Database) Data, CiteULike, Epinions, and Etsy to measure global accuracy, global loss, F-1 Score, and AUC (area under curve) values. Assessment through various techniques such as random forest, Naïve Bayes, and Logistic Regression on heterogeneous benchmark datasets indicates that random forest is the most effective method, achieving an accuracy rate exceeding 90%. Further, the proposed approach received a global accuracy above 95%, a global loss of 1.50%, an F-1 Score of 0.78, and an AUC value of 92%. Furthermore, the experiments conducted on distributed and global differential privacy (GDP) further optimize the system’s efficacy.

AdaLo: Adaptive learning rate optimizer with loss for classification

Gradient-based algorithms are frequently used to optimize neural networks, with various methods developed to enhance their performance. Among them, the adaptive moment estimation (Adam) optimizer is well-known for its effectiveness and ease of implementation. However, it suffers from poor generalization without a learning rate scheduler. Additionally, it has the disadvantage of a large computational burden because of individual learning rate term, as known as second-order moments of gradients. In this study, we propose a novel gradient descent algorithm called AdaLo, which stands for Adaptive Learning Rate Optimizer with Loss. AdaLo addresses two problems using its adaptive learning rate (ALR). Firstly, the proposed ALR adjusts the learning rate, based on the model's training progress, specifically the loss value. Therefore AdaLo's ALR effectively replaces traditional learning rate schedulers. Secondly, the ALR is a scalar global learning rate, reducing the computational burden. In addition, the stability of the proposed method is analyzed from the perspective of the learning rate. The superiority of AdaLo was proven by non-convex functions. Simulation results indicated that the proposed optimizer outperformed the Adam, AdaBelief, and diffGrad with regard to the training error and test accuracy.

Thermal and Dielectric Properties of EuF3: Gd @ Tryptophan

Abstract EuF3: Gd @ tryptophan was synthesized via aqueous solution route at room temperature. The XRD studies shows hexagonal phase of synthesized nanoparticles with lattice parameters a = b = 6.966 (A°), c = 7.322 (A°). The lattice parameter values are in good agreement with (JCPDS card no 32-0373). The particle size of nanoparticles was calculated using Debye-Scherer formula and it is found to be 73.54 nm. Nanoparticles has flake like morphology having size of the order of 56 nm – 71 nm with nanoparticles has average size of 13 nm. The TEM image shows globular aggregates with assorted size were also seen with diameter 56 nm – 71 nm from which the flower like star structures originates. The TGA/DTA analyses of synthesized sample were studied up to 1500°C at heating rate of 0.01°C – 100°C/min. The variation in dielectric constant ε′, dielectric loss ε″ and tangent loss (tan δ) with frequency (100 Hz – 5 MHz) was studied at room temperature. The dielectric constant and dielectric loss are found to decrease exponentially with frequency. The low value of dielectric loss at higher frequencies indicates its use in electronic industry.

Polymetallic red mud direct materialization strategy towards zero waste emission: Electromagnetic wave absorption conversion and sodium solidification

Red mud is an industrial hazardous alkaline solid waste, whereas it is rich in polymetallic components showing a high comprehensive utilization value. Direct materialization of red mud towards zero waste emission is a more reasonable and environmentally friendly strategy. In this work, a novel materialization approach of red mud via converting the polymetallic constituents into functional ferrite material by phase reconstruction with MnO2 was proposed. The mineral phase reconstruction, microstructure evolution, reaction interface observation, electromagnetic property, and the materialization conversion mechanism were investigated. It was found that the complicated phases in red mud were transferred to simple forms including the magnetic ferrites (Ca/Na/Al/Ti-bearing Mn ferrite) and weakly magnetic silicates ((Na,Al,Ca)2SiO3) after phase reconstruction. The reconstructed red mud after sintering at 1200 ℃ for 120min in air atmosphere had satisfactory electromagnetic property. Magnetism study demonstrated the soft magnetic property with maximum saturation magnetization of 34.22emu/g and coercivity of only 7.0Oe. Compared with the original red mud, effective electromagnetic wave absorption with reflection loss (RL) values of the reconstructed red mud below -20 dB were satisfied across a wider frequency bandwidth of 5-18GHz, and the minimum RL value was lower as -32.4dB with a thinner veneer thickness of 15mm, highlighting its excellent electromagnetic wave absorption performance. Moreover, Na solidification behaviors indicated that Na was solidified in the sintered product, which can reduce the possible environmental hazards of red mud alkalinity. The novel materialization treatment with zero waste emission exhibits a favorable application prospect with a large red mud batching ratio about 50wt%.

Enhanced microwave absorption properties of Ti3AlC2 particles modified by a facile preoxidation strategy

MAX phases are considered to be promising microwave absorbing materials in fifth-generation (5G) communications, but their high electrical conductivity causes impedance mismatching, weakening their ability to absorb microwaves. Here, we present a universal preoxidation strategy to improve the impedance matching and the microwave absorption performance of a Ti3AlC2 MAX phase absorbing material. The microwave absorption properties of Ti3AlC2 particles were enhanced after preoxidation at temperatures of 500-700 °C for only 30 min in air, as compared with unoxidized Ti3AlC2 particles. More interestingly, the 600 °C-preoxidized Ti3AlC2 material reached a minimum reflection loss (RLmin) value of -50.56 dB at 8.87 GHz, superior to -12.36 dB at 12.82 GHz for the original Ti3AlC2 material. The preoxidized Ti3AlC2 particles were covered by a thin oxidation layer comprising both amorphous TiO2 (a-TiO2) and rutile TiO2 (R-TiO2). The oxidation layer endows the preoxidized Ti3AlC2 particles with good impedance matching, and a large number of nano-interfaces of a-TiO2/R-TiO2 and micro-interfaces of a-TiO2/Ti3AlC2 also contribute to the dielectric loss mechanism, thus improving its microwave absorption ability. This work provides a practical strategy for the fundamental study and the optimal design of MAX microwave absorbing materials.

Thermal-Hydraulic Characteristics of an Earth Air Heat Exchanger: An experimental analysis

The pipe layout design has a great effect on the thermal-hydraulic characteristics of earth air heat exchanger (EAHE) systems. So, in the current study proposed four new buried pipe design configurations; low to high to low, high to low to high, spiral, and circular, in addition to the straight or uniform for comparison. These five configurations are tested and analyzed experimentally depending on the thermo-hydraulic performance for summer cooling requirements with variation of Re in the range of 18041 – 40843. The results show that changing the uniform design of the EAHE with fixed pipe length enhances both the heat transfer process and pressure loss values. At the same operating conditions, the circular design has the best performance compared to the other ones. For all pipe shapes, the cooling effect increases with the increase of air Re. The thermal- hydraulic performance of the circular design pipe is higher than that of the other pipe designs. The highest coefficient of performance (COP) average values that can be obtained at Re = 18041 is 3.05 for circular shape and enhances by 48.08 % compared to uniform shape. The effectiveness of EAHE with circular shape is improved by about 3.52 % compared to uniform shape at Re = 18041. Spiral design is optimum design based on the Nu value which reaches about 48 with enhancement 12.56% and 0.34 % compared to uniform and circular shapes respectively, at Re = 40843. The hydrothermal performance factor is highest in the case of circular shape with value about 21.9 at Re = 21257. By increasing Re, the drawbacks of the increasing in the total entropy generation are reflected negatively on the exergy efficiency. The specific cost of the circular shape at Re = 40843 is the optimum value by about 0.7 $/W. Spiral and circular shapes have a decrease of CO2 emissions less than uniform shape by percentage varied from 2.93 % to 13.84% for decrease in the Re from 21257 to 40843. It is concluded that using EAHE with four new shapes is highly efficient in increasing cooling capacity and energy consumption in buildings.

External validation of a multimodality deep-learning normal tissue complication probability model for mandibular osteoradionecrosis trained on 3D radiation distribution maps and clinical variables

Background and purposeWhile the inclusion of spatial dose information in deep learning (DL)-based normal-tissue complication probability (NTCP) models has been the focus of recent research studies, external validation is still lacking. This study aimed to externally validate a DL-based NTCP model for mandibular osteoradionecrosis (ORN) trained on 3D radiation dose distribution maps and clinical variables. Methods and materialsA 3D DenseNet-40 convolutional neural network (3D-mDN40) was trained on clinical and radiation dose distribution maps on a retrospective class-balanced matched cohort of 184 subjects. A second model (3D-DN40) was trained on dose maps only and both DL models were compared to a logistic regression (LR) model trained on DVH metrics and clinical variables. All models were externally validated by means of their discriminative ability and calibration on an independent dataset of 82 subjects. ResultsNo significant difference in performance was observed between models. In internal validation, these exhibited similar Brier scores around 0.2, Log Loss values of 0.6–0.7 and ROC AUC values around 0.7 (internal) and 0.6 (external). Differences in clinical variable distributions and their effect sizes were observed between internal and external cohorts, such as smoking status (0.6 vs. 0.1) and chemotherapy (0.1 vs. −0.5), respectively. ConclusionTo our knowledge, this is the first study to externally validate a multimodality DL-based ORN NTCP model. Utilising mandible dose distribution maps, these models show promise for enhancing spatial risk assessment and guiding dental and oncological decision-making, though further research is essential to address overfitting and domain shift for reliable clinical use.

Shape-Tunable Vanadium selenide/reduced graphene oxide Composites with Excellent Electromagnetic Wave Absorption Performance

The tunable energy gap and distinctive layered configuration of transition metal dichalcogenides (TMDs) has sparked considerable interest in their capabilities for electromagnetic wave absorption. As a significant TMD, vanadium selenide (VSe2) is characterized by a superior electrical conductivity (1 × 10-3 S/m) and an expanded interlayer distance, which are advantageous for electromagnetic wave absorption performance. Nevertheless, the current research on VSe2 in electromagnetic wave absorption is relatively limited. In this study, flower-like VSe2 and shape-tunable VSe2/reduced graphene oxide (rGO) composites were fabricated via a simple solvothermal method, and the effect of their morphology on electromagnetic wave absorption performances was investigated. The VSe2/rGO composites exhibited remarkable electromagnetic wave absorption properties at a thickness of 2.01 mm, with a reflection loss value (RL) of up to -79.50 dB, and an effective absorption bandwidth (EAB) of 5.2 GHz (1.45 mm). This research has identified a novel approach to the study of TMDs in the field of electromagnetic wave absorption.

Fabrication of hollow Fe3O4 microspheres encapsulated by single-walled carbon nanohorns for high-performance microwave absorption

Due to the increasingly serious electromagnetic radiation and pollution problems, it is urgent to develop high-performance electromagnetic wave absorbing materials. Herein, the hollow Fe3O4 microspheres encapsulated by single-walled carbon nanohorns (H-Fe3O4@SWCNHs) have been synthesized via facile solvothermal approach. The obtained H-Fe3O4@SWCNHs composite exhibits a unique hollow core-shell structure and superior microwave absorption performance. When the matching thickness is 2.0 mm, the composite shows a minimum reflection loss value of −42.2 dB at 13.6 GHz and a maximum effective absorption broadband value of 6.2 GHz covering from 11.8 to 18 GHz. The excellent microwave absorption performance of the H-Fe3O4@SWCNHs composite is mainly attributed to the unique hollow core-shell structure, optimal impedance matching, and synergistic effects of dielectric loss and magnetic loss. Therefore, the present work provides a new kind of ideal microwave absorbing material with light weight, thin thickness, wide bandwidth and strong absorption capability for high-performance microwave absorption.

Analysis and Prediction of Path Loss for Mobile Communication Lines in Nasarawa State Using Propagation Models

The design of future-generation mobile communication systems depends critically on the path loss prediction methods and their suitability to various signal propagation regions. Even though 5G has been launched in Nigeria, its deployment still faces significant challenges especially in the suburban, and non-urban regions which still depends on 4G. Many operators are still in the process of upgrading their existing 4G networks to support network coverage and higher speed data throughput. Due to the unique mix of Nasarawa State with diverse terrains, in this study, path loss prediction for the key telecommunication networks in Nasarawa State was carried out using Signal Strength Info App. Data was collected for a period of eight weeks (August, 2nd to September, 4th 2023) focusing on 4G LTE networks operating within the 1800 MHz frequency band. Analysis of path loss using empirical models such as Hata, Egli, and COST-231 Hata models were compared with the measured path loss in different terrains of urban, sub-urban, and non-urban environments. The Root Mean Square Error (RMSE) analysis was carried out between empirical and measured path losses. Results shows that, the order of mean predicted path loss at 5km was free space model (BN>CN>DN.AN), Hata model (DN>BN>CN>AN), Cos-231 Hata model (CN>AN>BN>DN), and Egli model (AN>BN>DN>CN). Hate model predicted the highest path loss values while Egli model predicted the lowest values. Overall, Egli model is found to be the most reliable and suitable path loss prediction model for entire Nasarawa State with RMSE value 5.99dBm, 2.90dBm and 3.14dBm for Nasarawa, Keffi and Akwanga LGA respectively. However, Cost-231 Hata model with RMSE value of 5.71dBm is suitable for path loss prediction in Karu due to its irregular terrain. The findings of this study are significant for practical applications in network planning, base station coverage, frequency allocation, and signal strength management, ensuring optimal mobile network performance across various terrains.

Improvement of microwave absorbing capability on the soft magnetic/hard magnetic/perovskite composites

Abstract This paper reports an increase in the ability to absorb microwaves from a composite made of combination between soft and hard magnetic materials, perovskite material, and an epoxy as matrix of composite, Ni0.5Zn0.5Fe2O4/Ba0.6Sr0.4Fe9.5Al0.5MnTiO19/FeTiO3/epoxy (NBFE) with a wt% ratio of 5:3:2:10. Composite samples were made using the mechanical alloying method with milling techniques and sintered at a temperature of 1000 oC for 5h. NBFE composites were studied to increase the ability to absorb microwaves at 2-18 GHz frequencies with thickness variations of 1 mm and 2 mm. The results obtained, the composite sample Ni0.5Zn0.5Fe2O4/Ba0.6Sr0.4Fe9.5Al0.5MnTiO19/FeTiO3/epoxy has the ability to absorb microwaves with the best reflection loss value -37.47 dB with a bandwidth almost reaches 11.32 GHz in the 2-18 GHz frequency range.

Life cycle cost approach involving steam transport model for insulation thickness optimization of steam pipes

Efficient steam pipe insulation is critical for minimizing heat loss during transportation, ensuring the economic viability of thermal processes or end-user applications. Unlike water-based systems, steam heat loss mechanisms are intricate, predominantly influenced by the transition of steam to condensate which introduces latent heat, thus significantly impacting overall heat loss. This study employs a life-cycle cost approach, integrating a comprehensive steam transport model to evaluate the optimal insulation thickness under varying temperature, flow rates and composite insulation schemes. The steam transport model incorporates a drainage energy loss term, providing a detailed depiction of the energy balance along the steam pipe. Results from the steam transportation simulation conducted on a single long pipe demonstrate a ∼4.6 % deviation between simulated and measured heat loss values. Furthermore, Life cycle cost analysis highlights the economically advantageous insulation configuration, comprising an aerogel blanket as the inner layer and glass wool as the outer layer. With increasing temperature, augmenting the proportion of aerogel blanket yields economic benefit, with the optimum thickness exhibiting a linear growth trend. Conversely, as the flow rate increases, the optimum insulation thickness remains constant.

Bamboo-Based Carbon/Co/CoO Heterojunction Structures Based on a Multi-Layer Periodic Matrix Array Can Be Used for Efficient Electromagnetic Attenuation

With the popularization of wireless communication, radar, and electronic devices, the hidden harm of electromagnetic radiation is becoming increasingly serious. The design of green biomass carbon-based interface heterojunctions based on lightweight porous materials can effectively protect against electromagnetic radiation hazards. In this work, we constructed an anisotropic heterojunction interface with magnetic and dielectric coupling based on a honeycomb-like periodic matrix multi-layer array repeating unit. The removal of lignin components from bamboo through oxidation enriches the impregnation pores and uniform adsorption sites of the magnetic medium. Further, in situ pyrolysis promotes the formation of a large number of electric dipoles at the interface between the magnetic medium and dielectric coupling inside the periodic cell carbon skeleton, enhancing interface polarization and relaxation. Local carrier traps and uneven electromagnetic density enhance dielectric and hysteresis losses, resulting in excellent impedance matching. Therefore, the obtained bamboo-based carbon multiphase composite absorbent has satisfactory electromagnetic loss characteristics. At a thickness of 1.55 mm, the effective absorption bandwidth reaches 5.1 GHz, and the minimum reflection loss (RL) value reaches −54.7 dB. In addition, the far-field radar simulation results show that the sample has an excellent RCS (radar cross-section) reduction of 33.3 dB·m2. This work provides new directions for the diversified development of green biomass and the optimization of the design of magnetic and dielectric coupling in periodic array structures.

Evaluation and Optimization of Cement Slurry Systems for Ultra-Deep Well Cementing at 220 °C

With the depletion of shallow oil and gas resources, wells are being drilled to deeper and deeper depths to find new hydrocarbon reserves. This study presents the selection and optimization process of the cement slurries to be used for the deepest well ever drilled in China, with a planned vertical depth of 11,100 m. The bottomhole circulating and static temperatures of the well were estimated to be 210 °C and 220 °C, respectively, while the bottomhole pressure was estimated to be 130 MPa. Laboratory tests simulating the bottomhole conditions were conducted to evaluate and compare the slurry formulations supplied by four different service providers. Test results indicated that the inappropriate use of a stirred fluid loss testing apparatus could lead to overdesign of the fluid loss properties of the cement slurry, which could, in turn, lead to abnormal gelation of the cement slurry during thickening time tests. The initial formulation given by different service providers could meet most of the design requirements, except for the long-term strength stability. The combined addition of crystalline silica and a reactive aluminum-bearing compound to oil well cement is critical for preventing microstructure coarsening and strength retrogression at 220 °C. Two of the finally optimized cement slurry formulations had thickening times more than 4 h, API fluid loss values less than 50 mL, sedimentation stability better than 0.02 g/cm3, and compressive strengths higher than 30 MPa during the curing period from 1 d to 30 d.

Diffusion Tensor Imaging of the Auditory Pathway in Prelingual Deaf Children in Comparison to Normal Hearing Children in the 1 to 7 Years of Age Group

Abstract Objective This article aims to determine the microstructural alterations in the auditory pathway in prelingual deaf children using diffusion tensor imaging (DTI)-derived parameters—fractional anisotropy (FA) and apparent diffusion coefficient (ADC), and secondarily to evaluate these changes in rubella and cytomegalovirus (CMV) positive cases. Method A consecutive series of consenting deaf and normal children between 1 and 7 years of age, forming the case and control groups, respectively, underwent DTI, audiological tests, and testing for rubella, CMV, and toxoplasma infections. FA and ADC were measured at four locations bilaterally: lateral lemniscus (LL), inferior colliculus, medial geniculate body, and auditory cortex (AC). Result The mean ADC values were higher and the mean FA values were lower in cases (19 males, 21 females, mean age 2.65 years) than the controls (21 males, 19 females, mean age 4.63 years) at all eight sites. Sixteen (40%), 17 (42.5%), and 7 (17.5%) cases had severe, severe to profound, and profound hearing loss, respectively, the FA and ADC values being significantly different for LL. For rubella and CMV immunoglobulin G, 20/40and 17/40 cases were positive, respectively, 11 for both, and none for toxoplasma. Significant decrease in FA was seen at LL and AC in rubella/CMV positive cases. Conclusion Microstructural changes are seen throughout the auditory pathway in prelingual deaf children, especially with rubella and/or CMV positive status. Further studies may pave the path to segregate out patient groups potentially more responsive to cochlear implant.